YOLOv7-Based Anomaly Detection Using Intensity and NG Types in Labeling in Cosmetic Manufacturing Processesopen access
- Authors
- Beak, S[Beak, Seunghyo]; Han, YH[Han, Yo-Han]; Moon, Y[Moon, Yeeun]; Lee, J[Lee, Jieun]; Jeong, J[Jeong, Jongpil]
- Issue Date
- Aug-2023
- Publisher
- MDPI
- Keywords
- deep learning; YOLOv7; object detection; anomaly detection
- Citation
- PROCESSES, v.11, no.8
- Indexed
- SCIE
SCOPUS
- Journal Title
- PROCESSES
- Volume
- 11
- Number
- 8
- URI
- https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/108118
- DOI
- 10.3390/pr11082266
- ISSN
- 2227-9717
- Abstract
- The advent of the Fourth Industrial Revolution has revolutionized the manufacturing sector by integrating artificial intelligence into vision inspection systems to improve the efficiency and quality of products. Supervised-learning-based vision inspection systems have emerged as a powerful tool for automated quality control in various industries. During visual inspection or final inspection, a human operator physically inspects a product to determine its condition and categorize it based on their know-how. However, the know-how-based visual inspection process is limited in time and space and is affected by many factors. High accuracy in vision inspection is highly dependent on the quality and precision of the labeling process. Therefore, supervised learning methods of 1-STAGE DETECTION, such as You Only Look Once (YOLO), are utilized in automated inspection to improve accuracy. In this paper, we proposed a labeling method that achieves the highest inspection accuracy among labeling methods such as NG intensity and NG intensity when performing anomaly detection using YOLOv7 in the cosmetics manufacturing process.
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Collections - Information and Communication Engineering > School of Electronic and Electrical Engineering > 1. Journal Articles
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